Lindsay Olson
2/23/2021
##
##
## | | level | ASD | TD | p | test |
## |:-------------------------:|:------:|:---------------------:|:----------------------:|:------:|:----:|
## | **n** | | 28 | 27 | | |
## | **SEX (%)** | Female | 6 (21.4) | 14 (51.9) | 0.039 | |
## | | Male | 22 (78.6) | 13 (48.1) | | |
## | **V1.Age (mean (SD))** | | 39.39 (13.77) | 39.56 (13.98) | 0.965 | |
## | **MEL_cat (mean (SD))** | | 3.52 (1.60) | 4.33 (1.05) | 0.049 | |
## | **INR (mean (SD))** | | 3.35 (2.79) | 4.37 (2.10) | 0.180 | |
## | **zipIncome (mean (SD))** | | 53477.39 (18001.70) | 57480.76 (15744.99) | 0.439 | |
## | **SES1 (mean (SD))** | | -0.40 (2.06) | 0.62 (1.76) | 0.054 | |
## | **ExpLang_T (mean (SD))** | | 31.67 (11.07) | 49.78 (11.10) | <0.001 | |
## | **RecLang_T (mean (SD))** | | 32.32 (11.85) | 53.85 (10.96) | <0.001 | |
## | **ELC_SS (mean (SD))** | | 71.96 (20.00) | 105.67 (15.78) | <0.001 | |
## | **TBV (mean (SD))** | | 1077031.79 (96937.48) | 1050164.41 (111924.89) | 0.345 | |
##
## Table: Participant Summary Table for Q>=4
##
##
## | | level | ASD | TD | p | test |
## |:-------------------------:|:------:|:----------------------:|:----------------------:|:------:|:----:|
## | **n** | | 36 | 31 | | |
## | **SEX (%)** | Female | 10 (27.8) | 14 (45.2) | 0.221 | |
## | | Male | 26 (72.2) | 17 (54.8) | | |
## | **V1.Age (mean (SD))** | | 39.03 (12.95) | 37.61 (14.20) | 0.671 | |
## | **MEL_cat (mean (SD))** | | 3.54 (1.50) | 4.36 (0.99) | 0.019 | |
## | **INR (mean (SD))** | | 3.46 (2.75) | 4.13 (2.14) | 0.319 | |
## | **zipIncome (mean (SD))** | | 54158.10 (18734.17) | 56118.68 (15004.41) | 0.675 | |
## | **SES1 (mean (SD))** | | -0.35 (1.97) | 0.54 (1.66) | 0.053 | |
## | **ExpLang_T (mean (SD))** | | 32.62 (11.76) | 48.77 (11.52) | <0.001 | |
## | **RecLang_T (mean (SD))** | | 32.49 (12.52) | 53.32 (11.13) | <0.001 | |
## | **ELC_SS (mean (SD))** | | 72.23 (19.97) | 104.94 (16.57) | <0.001 | |
## | **TBV (mean (SD))** | | 1076827.78 (100384.08) | 1045805.34 (115665.34) | 0.244 | |
##
## Table: Participant Summary Table for Q>=3
Q>=4 Kolmogorov Smirnov Test: D=0.27, p = 0.25 (distributions not significantly different from one another).
Q>=3 Kolmogorov Smirnov Test: D=0.21, p = 0.46 (distributions not significantly different from one another).
Q>=4 Kolmogorov Smirnov Test: D=0.42, p = 0.04 (distributions are significantly different from one another).
Q>=3 Kolmogorov Smirnov Test: D=0.36, p = 0.06 (distributions not significantly different from one another).
Q>=4 Kolmogorov Smirnov Test: D=0.36, p = 0.11 (distributions not significantly different from one another).
Q>=3 Kolmogorov Smirnov Test: D=0.32, p = 0.11 (distributions not significantly different from one another).
Q>=4 Kolmogorov Smirnov Test: D=0.22, p = 0.67 (distributions not significantly different from one another).
Q>=3 Kolmogorov Smirnov Test: D=0.2, p = 0.64 (distributions not significantly different from one another).
Q>=4 Kolmogorov Smirnov Test: D=0.09, p = 0.99 (distributions not significantly different from one another).
Q>=3 Kolmogorov Smirnov Test: D=0.12, p = 0.96 (distributions not significantly different from one another).
| rh_parsopercularis_lgi | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.48 | 2.46 – 4.50 | <0.001 |
| Income:Needs | 0.04 | 0.01 – 0.08 | 0.026 |
| TBV | 0.00 | -0.00 – 0.00 | 0.125 |
| Age | 0.01 | 0.00 – 0.02 | 0.027 |
| Observations | 42 | ||
| R2 / R2 adjusted | 0.342 / 0.290 | ||
Income to needs ratio predicts rh_parsopercularis_lgi, controlling for age and TBV in the Q>=4 dataset.
| rh_parsopercularis_lgi | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.74 | 2.91 – 4.58 | <0.001 |
| Income:Needs | 0.03 | 0.00 – 0.07 | 0.045 |
| TBV | 0.00 | -0.00 – 0.00 | 0.144 |
| Age | 0.01 | 0.00 – 0.01 | 0.031 |
| Observations | 52 | ||
| R2 / R2 adjusted | 0.289 / 0.244 | ||
Income to needs ratio predicts rh_parsopercularis_lgi, controlling for age and TBV in the Q>=3 dataset.
| lh_parsorbitalis_lgi | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 2.68 | 2.36 – 3.01 | <0.001 |
| Zip-Income | 0.00 | 0.00 – 0.00 | 0.018 |
| Age | 0.01 | 0.00 – 0.01 | 0.019 |
| Observations | 44 | ||
| R2 / R2 adjusted | 0.235 / 0.198 | ||
Zip-Income is a significant predictor of lh pars orbitalis LGI when controlling for age (removed TBV because it was not a significant predictor in the model with age and Zip-Income).
Dx not a significant predictor in this model, nor is the diagnosisXzipIncome interaction term.
| lh_parsorbitalis_lgi | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 2.66 | 2.38 – 2.94 | <0.001 |
| Zip-Income | 0.00 | 0.00 – 0.00 | 0.002 |
| Age | 0.01 | 0.00 – 0.01 | 0.014 |
| Observations | 55 | ||
| R2 / R2 adjusted | 0.267 / 0.239 | ||
Zip-Income is a significant predictor or lh pars orbitalis LGI when controlling for age (removed TBV because it was not a significant predictor in the model with age and Zip-Income).
Dx not a significant predictor in this model, nor is the diagnosisXzipIncome interaction term.
| rh_superiortemporal_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.38 | 3.17 – 3.58 | <0.001 |
| Income:Needs | 0.02 | 0.00 – 0.04 | 0.044 |
| Age | -0.01 | -0.01 – -0.00 | 0.001 |
| Sex | 0.03 | -0.06 – 0.12 | 0.449 |
| Observations | 46 | ||
| R2 / R2 adjusted | 0.275 / 0.223 | ||
INR Predicts LH middle temporal cortical thickness (CT), controlling for age and sex/gender in the Q>=4 dataset.
| lh_middletemporal_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.32 | 3.13 – 3.51 | <0.001 |
| Income:Needs | 0.02 | 0.00 – 0.04 | 0.020 |
| Age | -0.00 | -0.01 – -0.00 | 0.004 |
| Sex | 0.03 | -0.06 – 0.11 | 0.507 |
| Observations | 56 | ||
| R2 / R2 adjusted | 0.212 / 0.166 | ||
| lh_superiortemporal_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.09 | 2.90 – 3.28 | <0.001 |
| Income:Needs | 0.02 | 0.00 – 0.04 | 0.031 |
| Age | -0.00 | -0.01 – 0.00 | 0.096 |
| Sex | -0.01 | -0.09 – 0.07 | 0.801 |
| Observations | 56 | ||
| R2 / R2 adjusted | 0.118 / 0.067 | ||
| rh_parstriangularis_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 2.77 | 2.57 – 2.97 | <0.001 |
| Income:Needs | 0.02 | 0.00 – 0.04 | 0.025 |
| Age | -0.00 | -0.01 – -0.00 | 0.027 |
| Sex | 0.09 | 0.00 – 0.18 | 0.049 |
| Observations | 56 | ||
| R2 / R2 adjusted | 0.214 / 0.168 | ||
INR Predicts middle temporal gyrus CT as well as lh STG CT and rh pars triangularis CT in the Q>=3 dataset (only predicts middle temporal CT in the Q>=4 dataset).
| lh_parsopercularis_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.11 | 2.97 – 3.26 | <0.001 |
| SES1 | 0.02 | 0.00 – 0.04 | 0.022 |
| Age | -0.00 | -0.01 – -0.00 | <0.001 |
| Sex | 0.03 | -0.04 – 0.10 | 0.435 |
| Observations | 58 | ||
| R2 / R2 adjusted | 0.257 / 0.215 | ||
| rh_middletemporal_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.26 | 3.09 – 3.42 | <0.001 |
| SES1 | 0.02 | 0.00 – 0.04 | 0.035 |
| Age | -0.00 | -0.01 – -0.00 | 0.008 |
| Sex | 0.08 | -0.00 – 0.16 | 0.062 |
| Observations | 58 | ||
| R2 / R2 adjusted | 0.196 / 0.151 | ||
SES1 predicts lh pars opercularis CT (controlling for age and sex).
SES1 predicts rh middle temporal CT (controlling for age and sex).
TBV not a significant predictor of CT in tested regions, therefore removed as a covariate.
| lh_parsopercularis_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.09 | 2.96 – 3.23 | <0.001 |
| SES1 | 0.02 | -0.00 – 0.03 | 0.057 |
| Age | -0.00 | -0.01 – -0.00 | 0.001 |
| Sex | 0.02 | -0.05 – 0.08 | 0.638 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.185 / 0.147 | ||
| rh_middletemporal_CT | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 3.24 | 3.07 – 3.42 | <0.001 |
| SES1 | 0.01 | -0.01 – 0.04 | 0.222 |
| Age | -0.00 | -0.01 – 0.00 | 0.068 |
| Sex | 0.05 | -0.04 – 0.13 | 0.281 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.075 / 0.032 | ||
Associations between SES1 and pars opercularis / middle temporal gyrus are not significant in the Q>=3 dataset, controlling for age, sex/gender, and TBV.
| rh_parsorbitalis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 36.73 | -275.95 – 349.42 | 0.814 |
| INR | -12.69 | -23.72 – -1.66 | 0.025 |
| TBV | 0.00 | 0.00 – 0.00 | 0.001 |
| Age | 1.36 | -1.15 – 3.87 | 0.279 |
| Sex | -21.74 | -92.24 – 48.77 | 0.537 |
| Observations | 46 | ||
| R2 / R2 adjusted | 0.494 / 0.445 | ||
INR is associated with rh pars orbitalis area in the Q>=4 dataset, controlling for TBV, age, and sex (although neither age nor sex were significant predictors of RH pars orbitalis SA).
Dx is also not a significant predictor, and there is no significant Dx by INR interaction.
| rh_parsorbitalis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | -57.62 | -306.01 – 190.77 | 0.643 |
| INR | -11.64 | -21.62 – -1.67 | 0.023 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 0.89 | -1.39 – 3.17 | 0.438 |
| Sex | -34.39 | -90.34 – 21.56 | 0.223 |
| Observations | 56 | ||
| R2 / R2 adjusted | 0.551 / 0.516 | ||
INR is associated with rh pars orbitalis area in the Q>=3 dataset, controlling for TBV, age, and sex (although neither age nor sex were significant predictors of RH pars orbitalis SA).
Dx is also not a significant predictor, and there is no significant Dx by INR interaction.
| lh_parsorbitalis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | -32.25 | -207.46 – 142.95 | 0.714 |
| SES1 | -10.42 | -18.88 – -1.95 | 0.017 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 2.34 | 0.92 – 3.75 | 0.002 |
| Observations | 58 | ||
| R2 / R2 adjusted | 0.617 / 0.595 | ||
| lh_parsopercularis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | -58.25 | -594.31 – 477.81 | 0.828 |
| SES1 | -28.09 | -53.97 – -2.20 | 0.034 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 6.35 | 2.02 – 10.67 | 0.005 |
| Observations | 58 | ||
| R2 / R2 adjusted | 0.547 / 0.522 | ||
Neighborhood advantage is associated with left hemisphere pars orbitalis and pars opercularis SA, controlling for TBV and age.
No significant dx by SES1 interaction term, nor dx main effect.
| lh_parsorbitalis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | -57.59 | -225.59 – 110.41 | 0.496 |
| SES1 | -9.84 | -18.66 – -1.02 | 0.029 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 2.36 | 0.94 – 3.79 | 0.002 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.593 / 0.574 | ||
| lh_parsopercularis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 123.92 | -383.01 – 630.85 | 0.627 |
| SES1 | -17.55 | -44.16 – 9.06 | 0.192 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 5.60 | 1.30 – 9.91 | 0.012 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.455 / 0.430 | ||
| rh_parstriangularis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | -470.22 | -1057.97 – 117.54 | 0.115 |
| SES1 | -38.02 | -68.88 – -7.17 | 0.017 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 4.08 | -0.91 – 9.08 | 0.108 |
| Observations | 69 | ||
| R2 / R2 adjusted | 0.485 / 0.461 | ||
Neighborhood advantage associated with left hemisphere and pars orbitalis SA, as in the Q>=4 dataset,
Neighborhood advantage is not significantly associated with pars opercularis SA in the Q>=3 dataset (in contrast to Q>=4).
Neighborhood advantage is also associated with right hemisphere pars triangularis SA (not significant in the Q>=4 dataset
| lh_parsorbitalis_area | |||
|---|---|---|---|
| Coeffcient | Estimates | CI (95%) | p-Value |
| Intercept | 32.71 | -162.80 – 228.21 | 0.737 |
| Zip-Income | -0.00 | -0.00 – -0.00 | 0.039 |
| TBV | 0.00 | 0.00 – 0.00 | <0.001 |
| Age | 1.51 | -0.04 – 3.07 | 0.056 |
| Observations | 47 | ||
| R2 / R2 adjusted | 0.570 / 0.540 | ||
Zip-Income is negatively associated with lh pars orbitalis SA in the Q>=4 dataset. Visualizing the partial regression plot here also.